Post-Pandemic Needs of Unpaid Family and Friend Caregivers to Effectively Continue Caregiving Duties in one Northern Ontario Health Authority
Bibliographic record
Abstract
The Covid-19 pandemic had a significant impact on the support networks for older adults and caregivers as health and social care systems were forced to dramatically change the ways patients and clients interacted with providers, services, and programs. In Northern Ontario, caregivers are older, caring in more intense situations, more likely to be caring for multiple care recipients simultaneously and less likely to be in contact with health professionals. This research sought to explore the post-pandemic needs of caregivers in a Northern Ontario health catchment to better understand the needed supports. Using a collaborative and co-design approach with caregiver advisors within a qualitative description design, seven focus groups were conducted with 36 participants in total in February 2023. Reflexive thematic analysis was used to generate five themes from the transcripts: caregivers as the invisible but vital backbone of health and social care; amplified distress: navigating overwhelming demands; family fault lines exposed; contextualized care: the need for personalized supports; and empowering caregivers through training and supports. Our findings suggest that the pandemic significantly impacted the already vulnerable support networks for older adults and caregivers, as health and social care systems had to adapt to new restrictions and limitations. Caregivers were forced to take on additional responsibilities and cope with social isolation, leading to detrimental effects on their mental health and overall well-being.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".